Design-based distribution function estimation for stigmatized populations

In this paper, we discuss in a general framework the design-based estimation of population parameters when sensitive data are collected by randomized response techniques. We show in close detail the procedure for estimating the distribution function of a sensitive quantitative variable and how to estimate simultaneously the population prevalence of individuals bearing a stigmatizing attribute and the distribution function for the members belonging to the hidden group. The randomized response devices by Greenberg et al. (J Am Stat Assoc 66:243–250, 1971), Franklin (Commun Stat Theory Methods 18:489–505, 1989), and Singh et al. (Aust NZ J Stat 40:291–297 1998) are here considered as data-gathering tools.     

Circular economy,the transition of an incumbent focal firm: How to successfully reconcile environmental and economic sustainability?

Circular economy (CE) has garnered increasing attention in political circles and practitioner literature thanks to its potential to overcome the harmful consequences of linear patterns of growth. Nonetheless, the adoption of sustainable practices entails a holistic approach requiring businesses to make significant changes to their production, technologies and stakeholder management. These challenges resulted in limited progress in terms of the practical implementation of CE. This paper addresses the need for more literature focused on the pragmatic aspects of applying this paradigm to incumbent firms. The author presents the journey that Lucart S.p.A., a major European tissue paper manufacturer, has undertaken between 2014 and 2020 to apply CE principles to its practices. Insights from the transformation process, reconstructed through interviews with the firm's management and environmental impact data, suggest that to succeed it takes innovating (plants, products and marketing strategies) and setting up supply chains that reconcile environmental and economic sustainability.     

Models to date the business cycle: The Italian case

The problem of dating the business cycle has recently received many contributions, with a lot of proposed statistical methodologies, parametric and non-parametric. In general, these methods are not used in official dating, which is carried out by experts, who use their subjective evaluations of the state of economy. In this work we try to apply some statistical procedures to obtain an automatic dating of the Italian business cycle in the last 30 years, checking differences among various methodologies and with the ISAE chronology. The purpose of this exercise is to verify if purely statistical methods can reproduce the turning points detection proposed by economists, so that they could be fruitfully used in official dating. To this end parametric as well as non-parametric methods are employed. The analysis is carried out both aggregating results from single time series and directly in a multivariate framework. The different methods are also evaluated with respect to their ability to timely track (ex post) turning points.